How NOT to Train an Offensive Security AI Agent
This report discusses an unsuccessful experiment in training an AI model to solve offensive security challenges using a custom benchmark called TarantuBench. The experiment involved iterative training of AI agents on interactive cyber puzzles designed to measure offensive cybersecurity capabilities. Despite multiple approaches, the AI model did not improve beyond its baseline performance. The author shares detailed lessons learned about pitfalls in training such models, emphasizing the complexity of effective AI training for offensive security tasks.
AI Analysis
Technical Summary
The threat report details an experiment aimed at training an AI agent to improve offensive cybersecurity skills by solving a set of 100 verifiable web security scenarios (TarantuBench). The training attempts included reinforcement learning and supervised fine-tuning on model-generated solutions, but the AI consistently failed to surpass its initial capabilities. The author identifies multiple technical challenges and methodological errors encountered during training, such as insufficient and poorly structured data, lack of proper evaluation metrics, and ineffective training strategies. The experiment was conducted on cloud infrastructure but was discontinued due to cost and limited success. The report serves as a cautionary case study rather than describing an active vulnerability or exploit.
Potential Impact
There is no direct security impact or active exploit associated with this report. It documents research and experimentation in AI training for offensive security tasks that did not yield improved AI capabilities. No vulnerabilities or exploits are described, and no systems are reported as compromised or at risk. The content is primarily educational and methodological.
Mitigation Recommendations
No mitigation is required as this is not a vulnerability or active threat. The author recommends careful consideration of training data quality, evaluation metrics, and training methodologies when developing AI models for offensive security tasks. Practitioners should avoid the pitfalls described and await further research outcomes.
How NOT to Train an Offensive Security AI Agent
Description
This report discusses an unsuccessful experiment in training an AI model to solve offensive security challenges using a custom benchmark called TarantuBench. The experiment involved iterative training of AI agents on interactive cyber puzzles designed to measure offensive cybersecurity capabilities. Despite multiple approaches, the AI model did not improve beyond its baseline performance. The author shares detailed lessons learned about pitfalls in training such models, emphasizing the complexity of effective AI training for offensive security tasks.
Reddit Discussion
Last week I spent more time and money than I'm willing to admit trying to make a small AI model very good at CTFs.
Specifically, training it based on the benchmark I created - TarantuBench. That benchmark measures the offensive capabilities of artificial intelligence models using interactive cyber puzzles. Each such puzzle has a unique solution, so you can gauge whether the model succeeded or not through a direct check.
My thesis is the following - if the benchmark measures cyber capabilities, then perhaps it is possible to train a model based on it to perform such puzzles better.
The answer?
Maybe
Of course, I started the hard way. I set up a server in Google's cloud where the model would try to solve these puzzles over time, and learn from its mistakes and successes. GRPO, for those wondering.
It didn't work for an engineering reason - I wasn't convinced that my implementation of this algorithm for the benchmark I built was correct.
I switched to a simpler method. I let the model run on the entire benchmark, took all its solutions, and tried to train it to continue solving in that way and not in another way that leads to errors. SFT of course.
Two problems:
First of all, the data I built wasn't good. It took me (too) long to figure it out. I took the solutions as they were, without thinking too much about how I would re-feed them to the model so that it would really understand something from this data.
Then, I realized that I didn't have enough data. I didn't run the model enough times on the benchmark. At this point, between payments to Google's cloud, for the model, and for Cursor, I decided that I would end my investment in the experiment.
The result is that every time I trained the model, it failed to exceed its original performance, and sometimes even deteriorated.
What did I learn?
Don't train on solvers alone. Oracle scripts ≠ agent policy.
Don't count solves without counting labs. 450 solves on 2 labs is not abundance.
Don't distill a strong teacher into a weak student without student rollouts. Cross-model SFT is few-shot transfer.
Don't expect fork rows to replace episodes. Prefix→decision pairs don't teach horizon control.
Don't augment your way out of n≈10. Grounding filters and replay repair are hygiene, not data.
Don't split by run when labs repeat. Lab-disjoint or don't report generalization.
Don't chase chains before val singles lift. Composition needs components.
Don't trust train loss. Track val solve rate and per-lab regressions against base.
Don't skip the base arm. Every SFT eval should log base=SOLVED|FAIL per lab.
What does this mean?
That the experiment was unsuccessful - not that my thesis is wrong. I don't plan to end this saga here, but I will take a short break and am sharing with you what *not* to do when you approach training models.
Stay tuned, I'll try again soon.
Full experiment at tarantulabs.com
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
The threat report details an experiment aimed at training an AI agent to improve offensive cybersecurity skills by solving a set of 100 verifiable web security scenarios (TarantuBench). The training attempts included reinforcement learning and supervised fine-tuning on model-generated solutions, but the AI consistently failed to surpass its initial capabilities. The author identifies multiple technical challenges and methodological errors encountered during training, such as insufficient and poorly structured data, lack of proper evaluation metrics, and ineffective training strategies. The experiment was conducted on cloud infrastructure but was discontinued due to cost and limited success. The report serves as a cautionary case study rather than describing an active vulnerability or exploit.
Potential Impact
There is no direct security impact or active exploit associated with this report. It documents research and experimentation in AI training for offensive security tasks that did not yield improved AI capabilities. No vulnerabilities or exploits are described, and no systems are reported as compromised or at risk. The content is primarily educational and methodological.
Mitigation Recommendations
No mitigation is required as this is not a vulnerability or active threat. The author recommends careful consideration of training data quality, evaluation metrics, and training methodologies when developing AI models for offensive security tasks. Practitioners should avoid the pitfalls described and await further research outcomes.
Technical Details
- Source Type
- Subreddit
- ExploitDev+pwned+hacking
- Reddit Score
- 0
- Discussion Level
- minimal
- Content Source
- reddit_link_post
- Post Type
- link
- Domain
- null
- Newsworthiness Assessment
- {"score":27,"reasons":["external_link","established_author","very_recent"],"isNewsworthy":true,"foundNewsworthy":[],"foundNonNewsworthy":[]}
- Has External Source
- true
- Trusted Domain
- false
Threat ID: 6a374b169c760d8add52ad02
Added to database: 06/21/2026, 02:23:18 UTC
Last enriched: 06/21/2026, 02:23:22 UTC
Last updated: 06/21/2026, 04:37:40 UTC
Views: 6
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